Image segmentation by generalized hierarchical fuzzy C-means algorithm

Journal of Intelligent and Fuzzy Systems

Volumn 28, Issue 2, 2015, Pages 961-973

  Zheng, Yuhui ^a   Jeon, Byeungwoo ^c   Xu, Danhua ^a   Wu, Q M Jonathan ^a,b   Zhang, Hui ^a  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b UNIVERSITY OF WINDSOR   (Canada)

^c Sungkyunkwan University   (South Korea)

Author keywords

Fuzzy C means; generalized mean; hierarchical distance function; image segmentation; spatial constraint

Indexed keywords

ALGORITHMS; CLUSTERING ALGORITHMS; COPYING; FUZZY CLUSTERING; FUZZY SYSTEMS; PROBLEM SOLVING;

DISTANCE FUNCTIONS; EFFECTIVE ALGORITHMS; FUZZY C MEAN; GENERALIZED MEAN; LINEAR COMBINATIONS; MATHEMATICAL FORMULAS; SPATIAL CONSTRAINTS; SPATIAL INFORMATIONS;

IMAGE SEGMENTATION;

EID: 84921899998     PISSN: 10641246     EISSN: 18758967     Source Type: Journal    
DOI: 10.3233/IFS-141378     Document Type: Article

References (29)

1. J. Yu, General C-means clustering model. IEEE Trans Pattern Anal Mach Intell 27(8) (2005), 1197-1211
2. N.B. Karayiannis, Weighted fuzzy learning vector quantization and weighted generalized fuzzy c-means algorithms, Proc IEEE Conf Fuzzy Systems (1996), 773-779
3. S. Krinidis and V. Chatzis, A robust fuzzy local information Cmeans clustering algorithm. IEEE Trans Image Process 5(19) ( 2010), 1328-1337
4. M. Ahmed, S.Yamany, N. Mohamed, A. Farag andT. Moriarty, A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imag 21 ( 2002), 193-199
5. J. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York, 1981
6. S. Chen and D. Zhang, Robust image segmentation usingFCM with spatial constraints based on new Kernel-induced distance measure. IEEE Transactions on Systems, Man and Cybernetics 34(4) (2004), 1907-1916
7. D. Pham and J.L. Prince, An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities, Pattern Recognit Lett 20 (1999), 57-68
8. P. Kersten, Fuzzy order statistics and their application to fuzzy clustering. IEEE Trans Fuzzy Syst 7(6) (1999), 708-712
9. L. Bobrowski and J. Bezdek, C-means clustering with the l1 and l8 norms. IEEE Trans Syst, Man, Cybern 21(3) (1991), 545-554
10. R.J. Hathaway, J.C. Bezdek and Y. Hu, Generalized fuzzy cmeans clustering strategies using Lp norm distances. IEEE Trans Fuzzy Syst 8(5) (2000), 576-582
11. F. Klawonn and A. Keller, Fuzzy clustering based on modified distance measures. In Proc 3rd Int Symp Adv Intell Data Anal ( 1999), 291-302
12. S.P. Chatzis and T.A. Varvarigou, A fuzzy clustering approach toward hidden markov random field models for enhanced spatially constrained image segmentation. IEEE Trans Fuzzy Syst 16(5) (2008), 1351-1361
13. P. Clifford, Markov Random Fields in Statistics. In Disorder in Physical Systems. A Volume in Honour of John M. Hammersley on the Occasion of His 70th Birthday, G. Grimmett and D. Welsh, Eds. Oxford, U.K.: Clarendon Press, Oxford Science Publication, 1990
14. L.R. Rabiner,Atutorial on hidden markov models and selected applications in speech recognition, Proc IEEE 77(2) (1989), 257-286
15. Y. Zhang, M. Brady and S. Smith, Segmentation of brain MR images through a hidden markov random field model and the expectation maximization algorithm. IEEE Trans Med Imag 20(1) (2001), 45-57
16. J. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. Journal of Cybernetics 3 (1974), 32-57
17. K.S. Chuang, H.L. Tzeng, S. Chen, J. Wu and T.J. Chen, Fuzzy c-means clustering with spatial information for image segmentation, Comput Med Imag Graph 30(1) (2006), 9-15
18. W. Pedrycz, Conditional fuzzy clustering in the design of radial basis function neural network. IEEE Trans Neural Netw 9 ( 1998), 601-612
19. C. Wei and C. Fahn, The multisynapse neural network and its application to fuzzy clustering. IEEE Trans Neural Netw 13(3) ( 2002), 600-618
20. M.S. Yang, On a class of fuzzy classification maximum likelihood procedures, Fuzzy Sets Syst 57 (1993), 365-375
21. M. Jordan and R. Jacobs, Hierarchical mixtures of experts and theEMalgorithm. In Proc Int Joint Conf Neural Netw 2 (1993), 1339-1344
22. M.K. Titsias and A. Likas, Mixture of experts classification using a hierarchical mixture model, Neural Comput 14(9) ( 2002), 2221-2244
23. P. Orbanz and J.M. Buhmann,SARimages as mixtures of gaussian mixtures, IEEE Int'l Conf, Image Processing 2 (2005), 209-212
24. M. Di Zio, U. Guarnera and R. Rocci, A mixture of mixture models for a classification problem: The unity measure error, Computational Statistics and Data Analysis 51(5) (2007), 2573-2585
25. D. Martin, C. Fowlkes, D. Tal and J. Malik, A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In Proc 8th IEEE Int Conf Comput Vis, Vancouver, BC, Canada, 2 (2001), 416-423
26. R. Unnikrishnan and M. Hebert, Measures of similarity. In Proc IEEE Workshop Comput Vis Appl (2005), 394-400
27. U. Qamar, A dissimilarity measure based Fuzzy c-means ( FCM) clustering algorithm. Journal of Intelligent and Fuzzy Systems 26(1) (2014), 229-238
28. J.-Y. Leu, K.-K. Huang and Y.-M. Huang, Two novel fuzzy clustering methods for solving data clustering problems. Journal of Intelligent and Fuzzy Systems 26(2) (2014), 705-719
29. W.l. Cai, S.C. Chen and D.Q. Zhang, Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation, Pattern Recognit 40(3) (2007), 825-838